"""
brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
author: lzhbrian (https://lzhbrian.me)
date: 2020.1.5
note: code is heavily borrowed from 
	https://github.com/NVlabs/ffhq-dataset
	http://dlib.net/face_landmark_detection.py.html

requirements:
	apt install cmake
	conda install Pillow numpy scipy
	pip install dlib
	# download face landmark model from:
	# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
"""
from argparse import ArgumentParser
import time
import numpy as np
import PIL
import PIL.Image
import os
import scipy
import scipy.ndimage
import dlib
import multiprocessing as mp
import math

#from configs.paths_config import model_paths
SHAPE_PREDICTOR_PATH = 'shape_predictor_68_face_landmarks.dat'#model_paths["shape_predictor"]


def get_landmark(filepath, predictor):
	"""get landmark with dlib
	:return: np.array shape=(68, 2)
	"""
	detector = dlib.get_frontal_face_detector()

	img = dlib.load_rgb_image(filepath)
	dets = detector(img, 1)

	for k, d in enumerate(dets):
		shape = predictor(img, d)

	t = list(shape.parts())
	a = []
	for tt in t:
		a.append([tt.x, tt.y])
	lm = np.array(a)
	return lm


def align_face(filepath, predictor):
	"""
	:param filepath: str
	:return: PIL Image
	"""

	lm = get_landmark(filepath, predictor)

	lm_chin = lm[0: 17]  # left-right
	lm_eyebrow_left = lm[17: 22]  # left-right
	lm_eyebrow_right = lm[22: 27]  # left-right
	lm_nose = lm[27: 31]  # top-down
	lm_nostrils = lm[31: 36]  # top-down
	lm_eye_left = lm[36: 42]  # left-clockwise
	lm_eye_right = lm[42: 48]  # left-clockwise
	lm_mouth_outer = lm[48: 60]  # left-clockwise
	lm_mouth_inner = lm[60: 68]  # left-clockwise

	# Calculate auxiliary vectors.
	eye_left = np.mean(lm_eye_left, axis=0)
	eye_right = np.mean(lm_eye_right, axis=0)
	eye_avg = (eye_left + eye_right) * 0.5
	eye_to_eye = eye_right - eye_left
	mouth_left = lm_mouth_outer[0]
	mouth_right = lm_mouth_outer[6]
	mouth_avg = (mouth_left + mouth_right) * 0.5
	eye_to_mouth = mouth_avg - eye_avg

	# Choose oriented crop rectangle.
	x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
	x /= np.hypot(*x)
	x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
	y = np.flipud(x) * [-1, 1]
	c = eye_avg + eye_to_mouth * 0.1
	quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
	qsize = np.hypot(*x) * 2

	# read image
	img = PIL.Image.open(filepath)

	output_size = 256
	transform_size = 256
	enable_padding = True

	# Shrink.
	shrink = int(np.floor(qsize / output_size * 0.5))
	if shrink > 1:
		rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
		img = img.resize(rsize, PIL.Image.ANTIALIAS)
		quad /= shrink
		qsize /= shrink

	# Crop.
	border = max(int(np.rint(qsize * 0.1)), 3)
	crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
			int(np.ceil(max(quad[:, 1]))))
	crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
			min(crop[3] + border, img.size[1]))
	if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
		img = img.crop(crop)
		quad -= crop[0:2]

	# Pad.
	pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
		   int(np.ceil(max(quad[:, 1]))))
	pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
		   max(pad[3] - img.size[1] + border, 0))
	if enable_padding and max(pad) > border - 4:
		pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
		img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
		h, w, _ = img.shape
		y, x, _ = np.ogrid[:h, :w, :1]
		mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
						  1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
		blur = qsize * 0.02
		img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
		img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
		img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
		quad += pad[:2]

	# Transform.
	img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
	if output_size < transform_size:
		img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)

	# Save aligned image.
	return img


def chunks(lst, n):
	"""Yield successive n-sized chunks from lst."""
	for i in range(0, len(lst), n):
		yield lst[i:i + n]


def extract_on_paths(file_paths):
	predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH)
	pid = mp.current_process().name
	print('\t{} is starting to extract on #{} images'.format(pid, len(file_paths)))
	tot_count = len(file_paths)
	count = 0
	for file_path, res_path in file_paths:
		count += 1
		if count % 100 == 0:
			print('{} done with {}/{}'.format(pid, count, tot_count))
		try:
			res = align_face(file_path, predictor)
			res = res.convert('RGB')
			os.makedirs(os.path.dirname(res_path), exist_ok=True)
			res.save(res_path)
		except Exception:
			continue
	print('\tDone!')


def parse_args():
	parser = ArgumentParser(add_help=False)
	parser.add_argument('--num_threads', type=int, default=1)
	parser.add_argument('--root_path', type=str, default='')
	args = parser.parse_args()
	return args


def run(args):
	root_path = args.root_path
	out_crops_path = root_path + '_crops'
	if not os.path.exists(out_crops_path):
		os.makedirs(out_crops_path, exist_ok=True)

	file_paths = []
	for root, dirs, files in os.walk(root_path):
		for file in files:
			file_path = os.path.join(root, file)
			fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path))
			res_path = '{}.jpg'.format(os.path.splitext(fname)[0])
			if os.path.splitext(file_path)[1] == '.txt' or os.path.exists(res_path):
				continue
			file_paths.append((file_path, res_path))

	file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads))))
	print(len(file_chunks))
	pool = mp.Pool(args.num_threads)
	print('Running on {} paths\nHere we goooo'.format(len(file_paths)))
	tic = time.time()
	pool.map(extract_on_paths, file_chunks)
	toc = time.time()
	print('Mischief managed in {}s'.format(toc - tic))


if __name__ == '__main__':
	args = parse_args()
	run(args)
